C6009 Master of Data Science

The course comprises 96 credit points structured into three parts: Part A. Foundation studies, Part B. Core studies and Part C. Applied studies. 

Part A. Foundation studies

These studies will provide an orientation to the field of data science at graduate level. They are intended for students whose previous qualification is not in a cognate field.

Part B. Core studies

These studies draw on best practices within the broad realm of data science practice and research. You will gain a critical understanding of theoretical and practical issues relating to data science.

Part C. Applied studies

The focus of these studies is professional or scholarly work that can contribute to a portfolio of professional development. You have two options:

• a program of coursework involving advanced study and an industry experience studio project.
• a research pathway including a thesis. If you wish to use this master's course as a pathway to a higher degree by research you should take this first option.

Master's entry points

Depending on prior qualifications you may receive entry level credit (a form of block credit) which determines your point of entry to the course:

• If you are admitted at entry level 1 you complete 96 credit points, comprising Part A, Part B and Part C.
• If you are admitted at entry level 2 you complete 72 credit points, comprising Part B and Part C.

Note: If you are eligible for credit for prior studies you may elect not to receive the credit and complete one of the higher credit-point options.

Course progression map

The course progression map provides guidance on unit enrolment for each semester of study.

The course comprises 96 points structured into three parts: Part A. Foundation studies (24 points), Part B. Core studies (48 points) and Part C. Applied studies (24 points). 

Units are 6 points unless otherwise stated.

Part A. Foundation studies (24 points)

You must complete:

• ITI9132 Introduction to databases
• ITI9136 Introduction to Python programming
• ITI9137 Introduction to computer architecture and networks
• ITI9004 Mathematical foundations for data science and AI

Part B. Core studies (48 points)

You must complete the following units (42 credit points):

• ITI5057 Project management
• ITI5125 IT research methods
• ITI5145 Introduction to data science
• ITI5147 Data exploration and visualisation
• ITI5196 Data wrangling
• ITI5197 Statistical data modelling
• ITI5202 Data processing for big data

Specified elective studies

You must complete one of the following units (6 credit points):

• ITI5149 Applied data analysis
• ITI5201 Machine learning
• ITI5212 Data analysis for semi-structured data

Part C. Applied studies (24 points)

You must complete either the Industry experience pathway or Research pathway:

a. Industry experience pathway

• ITI5120 Industry experience studio project 1
• ITI5121 Industry experience studio project 2
• ITI5122 Professional practice
• one level 5 elective unit (6 points). 

b. Research pathway

• ITI5126 Masters thesis part 1
• ITI5127 Masters thesis part 2
• ITI5128 Masters thesis final
• ITI5122 Professional practice

Enrolment in the research units is dependent on available supervisors and projects. Eligible students will be ranked based on their entire academic record and assessed for suitability to undertake the research component of this program.

Note 2: To be eligible for the research option, you must have successfully completed at least 24 credit points of level 5 FIT-coded units; and have an overall average of at least 80% across all Level 5 units; and must have have achieved at least a distinction (75%) in FIT5125 IT research methods; and achieved an overall course WAM of 70%. 

If you have a WAM between 75-79% across all Level 5 units you must have successfully completed at least 24 credit points of level 5 FIT-coded units; and demonstrated research capability with written support from a prospective supervisor; and must have achieved at least a distinction (75%) in FIT5125 IT research methods; and achieved an overall course WAM of 70%.